import numpy as np
import pandas as pd
import calendar
import datetime
import os

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
import matplotlib.colors as mcolors
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

Home / Business Analytics and Modeling

Instacart Market Basket Analysis Dataset

In this article, we work with the Instacart Market Basket Analysis Dataset from Kaggle. The dataset can downloaded from Kaggle.com or from instacart.com.

Data Description

The dataset for this competition is a relational set of files describing customers' orders over time. The goal of the competition is to predict which products will be in a user's next order. The dataset is anonymized and contains a sample of over 3 million grocery orders from more than 200,000 Instacart users. For each user, we provide between 4 and 100 of their orders, with the sequence of products purchased in each order. We also provide the week and hour of the day the order was placed and a relative measure of time between orders. For more information, see the blog post accompanying its public release.

Data Dictionary

The data dictionary is available here.

orders (3.4m rows, 206k users):

• order_id: order identifier
• user_id: customer identifier
• eval_set: which evaluation set this order belongs in (see SET described below)
• order_number: the order sequence number for this user (1 = first, n = nth)
• order_dow: the day of the week the order was placed on
• order_hour_of_day: the hour of the day the order was placed on
• days_since_prior: days since the last order, capped at 30 (with NAs for order_number = 1)

products (50k rows):

• product_id: product identifier
• product_name: name of the product
• aisle_id: foreign key
• department_id: foreign key

aisles (134 rows):

• aisle_id: aisle identifier
• aisle: the name of the aisle

deptartments (21 rows):

• department_id: department identifier
• department: the name of the department

order_products__SET (30m+ rows):

• order_id: foreign key
• product_id: foreign key
• add_to_cart_order: order in which each product was added to cart
• reordered: 1 if this product has been ordered by this user in the past, 0 otherwise

where SET is one of the four following evaluation sets (eval_set in orders):

• "prior": orders prior to that users most recent order (~3.2m orders)
• "train": training data supplied to participants (~131k orders)
• "test": test data reserved for machine learning competitions (~75k orders)

def Header(Text, L = 100, C1 = Back.BLUE, C2 = Fore.BLUE):
    print(C1 + Fore.WHITE + Style.NORMAL + Text + Style.RESET_ALL + ' ' + C2 +
          Style.NORMAL +  (L- len(Text) - 1)*'=' + Style.RESET_ALL)
def Line(L=100, C = Fore.BLUE): print(C + Style.NORMAL + L*'=' + Style.RESET_ALL)

PATH = 'Instacart'
Files = os.listdir(PATH)
Files.remove('sample_submission.csv')
Files_Info = pd.DataFrame()
for i in range(len(Files)):
    Header(Files[i])
    Temp = pd.read_csv(os.path.join(PATH, Files[i]))
    display(Temp.head(5))
    Files_Info = Files_Info.append(pd.DataFrame({'File':[Files[i]], 'Number of Instances': [Temp.shape[0]],
                                                 'Number of Attributes': [Temp.shape[1]]}), ignore_index = True)
    filename = Files[i].split('.')[0]
    globals() [filename] = pd.read_csv(os.path.join(PATH, Files[i]))
Line()
display(Files_Info.style.hide_index())
Line()
del Files, i, Temp

aisles.csv =========================================================================================

	aisle_id	aisle
0	1	prepared soups salads
1	2	specialty cheeses
2	3	energy granola bars
3	4	instant foods
4	5	marinades meat preparation

departments.csv ====================================================================================

	department_id	department
0	1	frozen
1	2	other
2	3	bakery
3	4	produce
4	5	alcohol

orders.csv =========================================================================================

	order_id	user_id	eval_set	order_number	order_dow	order_hour_of_day	days_since_prior_order
0	2539329	1	prior	1	2	8	NaN
1	2398795	1	prior	2	3	7	15.0
2	473747	1	prior	3	3	12	21.0
3	2254736	1	prior	4	4	7	29.0
4	431534	1	prior	5	4	15	28.0

order_products_prior.csv ===========================================================================

	order_id	product_id	add_to_cart_order	reordered
0	2	33120	1	1
1	2	28985	2	1
2	2	9327	3	0
3	2	45918	4	1
4	2	30035	5	0

order_products_train.csv ===========================================================================

	order_id	product_id	add_to_cart_order	reordered
0	1	49302	1	1
1	1	11109	2	1
2	1	10246	3	0
3	1	49683	4	0
4	1	43633	5	1

products.csv =======================================================================================

	product_id	product_name	aisle_id	department_id
0	1	Chocolate Sandwich Cookies	61	19
1	2	All-Seasons Salt	104	13
2	3	Robust Golden Unsweetened Oolong Tea	94	7
3	4	Smart Ones Classic Favorites Mini Rigatoni Wit...	38	1
4	5	Green Chile Anytime Sauce	5	13

====================================================================================================

File	Number of Instances	Number of Attributes
aisles.csv	134	2
departments.csv	21	2
orders.csv	3421083	7
order_products_prior.csv	32434489	4
order_products_train.csv	1384617	4
products.csv	49688	4

====================================================================================================

Exploratory Data Analysis

Most Ordered Products

fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(14, 6*3))
## The most ordered Products
Top_number = 15
Header('The Most Ordered Products', L = 100, C1 = Back.GREEN, C2 = Fore.GREEN)
Most_Ordered_Products = order_products_prior.groupby(['product_id'])['product_id'].agg({'count'})
Most_Ordered_Products.columns = ['Count']
Most_Ordered_Products = Most_Ordered_Products.reset_index(drop = False).sort_values(by='Count', ascending=False)
Most_Ordered_Products = pd.merge(Most_Ordered_Products, products, on='product_id', how='right')
Most_Ordered_Products = Most_Ordered_Products.reset_index(drop=True).dropna()
Most_Ordered_Products['Count'] = Most_Ordered_Products['Count'].astype(int)
display(Most_Ordered_Products.head(Top_number))

_ = sns.barplot(ax = ax[0], y='product_name', x='Count', palette='PuBu',
                edgecolor='SkyBlue',  hatch="///", data=Most_Ordered_Products.head(Top_number))
_ = sns.barplot(ax = ax[0], y='product_name', x='Count', facecolor = 'None',
                edgecolor='k',data=Most_Ordered_Products.head(Top_number))
_ = ax[0].set_title('Top %s Ordered Products' % Top_number)
_ = ax[0].set_xlim([0, 5e5])
_ = ax[0].set_ylabel('Products')
_ = ax[0].set_xlabel('Count')

## The most Common Order Size
Top_number = 30
Header('The Most Frequent Order Size', L = 100, C1 = Back.GREEN, C2 = Fore.GREEN)
Most_Ordered = order_products_prior.groupby(['order_id'])['product_id'].agg({'count'})
Most_Ordered.columns = ['Number of Products in an Order']
Most_Ordered = Most_Ordered.reset_index(drop = False).\
               groupby(['Number of Products in an Order'])['Number of Products in an Order'].agg({'count'})
Most_Ordered.columns = ['Count']
Most_Ordered = Most_Ordered.reset_index(drop = False).sort_values(by='Count', ascending=False)
display(Most_Ordered.head(Top_number).set_index('Number of Products in an Order').T)

_ = sns.barplot(ax = ax[1], x='Number of Products in an Order', y='Count', palette='OrRd',
                edgecolor='DarkOrange',  hatch="///", data= Most_Ordered.head(Top_number))
_ = sns.barplot(ax = ax[1], x='Number of Products in an Order', y='Count', facecolor = 'None',
                edgecolor='k',  data= Most_Ordered.head(Top_number))

_ = ax[1].set_title('%s Most Frequent Order Size' % Top_number)
_ = ax[1].set_ylim([0, 2.5e5])

## The Most Reordered Products
Top_number = 15
Header('The Most Reordered Products', L = 100, C1 = Back.GREEN, C2 = Fore.GREEN)
Temp = order_products_prior.loc[order_products_prior.reordered ==1]
Most_ReOrdered_Products = Temp.groupby(['product_id'])['product_id'].agg({'count'})
Most_ReOrdered_Products.columns = ['Count']
Most_ReOrdered_Products = Most_ReOrdered_Products.reset_index(drop = False).sort_values(by='Count', ascending=False)
Most_ReOrdered_Products = pd.merge(Most_ReOrdered_Products, products, on='product_id', how='right')
Most_ReOrdered_Products = Most_ReOrdered_Products.reset_index(drop=True).dropna()
Most_ReOrdered_Products['Count'] = Most_ReOrdered_Products['Count'].astype(int)

display(Most_ReOrdered_Products.head(Top_number))

_ = sns.barplot(ax = ax[2], y='product_name', x='Count', palette='PiYG',
                edgecolor='SkyBlue',  hatch="///", data=Most_ReOrdered_Products.head(Top_number))
_ = sns.barplot(ax = ax[2], y='product_name', x='Count', facecolor = 'None',
                edgecolor='k',data=Most_ReOrdered_Products.head(Top_number))
_ = ax[2].set_title('Top %s Reordered Products' % Top_number)
_ = ax[2].set_xlim([0, 4.5e5])
_ = ax[2].set_ylabel('Products')
_ = ax[2].set_xlabel('Count')

The Most Ordered Products ==========================================================================

	product_id	Count	product_name	aisle_id	department_id
0	1	1852	Chocolate Sandwich Cookies	61	19
1	2	90	All-Seasons Salt	104	13
2	3	277	Robust Golden Unsweetened Oolong Tea	94	7
3	4	329	Smart Ones Classic Favorites Mini Rigatoni Wit...	38	1
4	5	15	Green Chile Anytime Sauce	5	13
5	6	8	Dry Nose Oil	11	11
6	7	30	Pure Coconut Water With Orange	98	7
7	8	165	Cut Russet Potatoes Steam N' Mash	116	1
8	9	156	Light Strawberry Blueberry Yogurt	120	16
9	10	2572	Sparkling Orange Juice & Prickly Pear Beverage	115	7
10	11	104	Peach Mango Juice	31	7
11	12	246	Chocolate Fudge Layer Cake	119	1
12	13	9	Saline Nasal Mist	11	11
13	14	17	Fresh Scent Dishwasher Cleaner	74	17
14	15	4	Overnight Diapers Size 6	56	18

The Most Frequent Order Size =======================================================================

Number of Products in an Order	5	6	4	7	3	8	2	9	10	1	...	21	22	23	24	25	26	27	28	29	30
Count	228330	227675	222081	220006	207027	203374	186993	184347	165550	156748	...	41863	36368	31672	27065	23613	20283	17488	15102	13033	11251

1 rows × 30 columns

The Most Reordered Products ========================================================================

	product_id	Count	product_name	aisle_id	department_id
0	1	1136	Chocolate Sandwich Cookies	61	19
1	2	12	All-Seasons Salt	104	13
2	3	203	Robust Golden Unsweetened Oolong Tea	94	7
3	4	147	Smart Ones Classic Favorites Mini Rigatoni Wit...	38	1
4	5	9	Green Chile Anytime Sauce	5	13
5	6	3	Dry Nose Oil	11	11
6	7	12	Pure Coconut Water With Orange	98	7
7	8	83	Cut Russet Potatoes Steam N' Mash	116	1
8	9	82	Light Strawberry Blueberry Yogurt	120	16
9	10	1304	Sparkling Orange Juice & Prickly Pear Beverage	115	7
10	11	59	Peach Mango Juice	31	7
11	12	126	Chocolate Fudge Layer Cake	119	1
12	13	3	Saline Nasal Mist	11	11
13	14	2	Fresh Scent Dishwasher Cleaner	74	17
14	15	1	Overnight Diapers Size 6	56	18

Orders Distributions

Header('Orders Distributions (Week)', L = 100)
Orders_Distributions_Week = orders.groupby(['order_dow'])['order_dow'].agg({'count'})
Orders_Distributions_Week.index = calendar.day_name[0:7]
Orders_Distributions_Week.reset_index(inplace = True, drop = False)
Orders_Distributions_Week.columns =['Day of Week', 'Count']
display(Orders_Distributions_Week.set_index('Day of Week').T)

Header('Orders Distributions (Hours)', L = 100)
Orders_Distributions_Hours = orders.groupby(['order_hour_of_day'])['order_hour_of_day'].agg({'count'}).reset_index(drop = False)
Orders_Distributions_Hours.columns = ['Hour of Day', 'Count']
display(Orders_Distributions_Hours.set_index('Hour of Day').T)
        
fig = plt.figure(figsize=(13, 6), constrained_layout=True)
ax = np.zeros(2, dtype = 'object')
gs = fig.add_gridspec(1, 3)
ax[0] = fig.add_subplot(gs[0])
ax[1] = fig.add_subplot(gs[1:])

# Left Plot
_ = sns.barplot(ax = ax[0], y='Count', x='Day of Week', palette='summer',
                edgecolor='LimeGreen',  hatch="O", data= Orders_Distributions_Week)
_ = sns.barplot(ax = ax[0], y='Count', x='Day of Week', facecolor = 'None',
                edgecolor='k',data= Orders_Distributions_Week)
_ = ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=90)
_ = ax[0].set_ylim([0, 7e5])

# Right Plot
_ = sns.barplot(ax = ax[1], y='Count', x='Hour of Day', palette='cool',
                edgecolor='RoyalBlue',  hatch="*", data= Orders_Distributions_Hours)
_ = sns.barplot(ax = ax[1], y='Count', x='Hour of Day', facecolor = 'None',
                edgecolor='k',data= Orders_Distributions_Hours)
_ = ax[1].set_ylim([0, 3e5])

fig.tight_layout()

Orders Distributions (Week) ========================================================================

Day of Week	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday
Count	600905	587478	467260	436972	426339	453368	448761

Orders Distributions (Hours) =======================================================================

Hour of Day	0	1	2	3	4	5	6	7	8	9	...	14	15	16	17	18	19	20	21	22	23
Count	22758	12398	7539	5474	5527	9569	30529	91868	178201	257812	...	283042	283639	272553	228795	182912	140569	104292	78109	61468	40043

1 rows × 24 columns

Most orders take place on Mondays and Tuesdays. Besides, between 9:00 AM and 5:00 PM, most orders take place daily.

Orders_Distributions_Total = (orders.pivot_table(index='order_dow', columns='order_hour_of_day',
                                                 values='order_id', aggfunc='count').fillna(0)/1000).round(2)
Orders_Distributions_Total.index=calendar.day_name[0:7]
Orders_Distributions_Total.T.index.name = 'Hour of Day'
# display(Orders_Distributions_Total)
fig, ax = plt.subplots(figsize=(15,6))
_ = sns.heatmap(Orders_Distributions_Total, annot=True, cmap =sns.color_palette("RdYlGn", n_colors=10),
                  annot_kws={"size": 12}, linewidths=.5, ax=ax, vmin=0, vmax=60,
                  cbar_kws={ 'label': 'Number of Orders $\\times$ $10^3$', "aspect":30, "shrink": .5}) 
_ = ax.set_ylabel('Week Days')
_ = ax.set_xlabel('Hour of the day')
_ = ax.set_aspect(1)
_ = fig.tight_layout()

Between 9:00 AM and 5:00 PM, most orders take place on Mondays and Tuesdays.

Days after the First Order

Days_since_prior_order_summary = orders.days_since_prior_order.value_counts().sort_index().to_frame('Count').reset_index()
Days_since_prior_order_summary.columns = ['Days Since Prior Order', 'Count']
Days_since_prior_order_summary['Days Since Prior Order'] = Days_since_prior_order_summary['Days Since Prior Order'].astype(int)
#
fig = plt.figure(figsize=(13.5, 6))
ax = np.zeros(2, dtype = 'object')
gs = fig.add_gridspec(1, 3)
ax[0] = fig.add_subplot(gs[0])
ax[1] = fig.add_subplot(gs[1:])

# Left Plot
_ = ax[0].boxplot(orders.days_since_prior_order.dropna(), 0, 'rs', 0)
_ = ax[0].set_xlabel('Days since prior order')
_ = ax[0].set_title('Distribution of The Number\nof Days Since Prior Order')
_ = ax[0].set_xlim([0, 20])

# Right Plot
_ = sns.barplot(ax = ax[1], y='Count', x='Days Since Prior Order', palette='summer',
                edgecolor='DarkGreen',  hatch="..", data= Days_since_prior_order_summary)
_ = sns.barplot(ax = ax[1], y='Count', x='Days Since Prior Order', facecolor = 'None',
                edgecolor='k', data= Days_since_prior_order_summary)
_ = ax[1].set_ylim([0, 4e5])
_ = ax[1].set_xlabel('Days Since Prior Order')
_ = ax[1].set_title('Distribution of The Number\nof Days Since Prior Order')

fig.tight_layout()

Usually, customers order from 5 days to 15 days after the prior order. The majority of orders take place after a week and a month after the prior order.

Products Distributions

Products_Detailed  = pd.merge(left =pd.merge(left=products, right= departments, how='left'), right=aisles, how='left')
Products_Detailed.columns = [x.replace('_',' ').title().replace('Id','ID') for x in Products_Detailed.columns.tolist()]
Products_Detailed['Department'] = Products_Detailed['Department'].map(lambda x: x.title())
Products_Detailed['Aisle'] = Products_Detailed['Aisle'].map(lambda x: x.title())
Products_Detailed.head(10).style.hide_index()

Product ID	Product Name	Aisle ID	Department ID	Department	Aisle
1	Chocolate Sandwich Cookies	61	19	Snacks	Cookies Cakes
2	All-Seasons Salt	104	13	Pantry	Spices Seasonings
3	Robust Golden Unsweetened Oolong Tea	94	7	Beverages	Tea
4	Smart Ones Classic Favorites Mini Rigatoni With Vodka Cream Sauce	38	1	Frozen	Frozen Meals
5	Green Chile Anytime Sauce	5	13	Pantry	Marinades Meat Preparation
6	Dry Nose Oil	11	11	Personal Care	Cold Flu Allergy
7	Pure Coconut Water With Orange	98	7	Beverages	Juice Nectars
8	Cut Russet Potatoes Steam N' Mash	116	1	Frozen	Frozen Produce
9	Light Strawberry Blueberry Yogurt	120	16	Dairy Eggs	Yogurt
10	Sparkling Orange Juice & Prickly Pear Beverage	115	7	Beverages	Water Seltzer Sparkling Water

Products Distributions in Each Department

Products_by_Department = Products_Detailed.groupby('Department')['Product ID'].count().\
                            to_frame('Total Products').reset_index(drop = False)
Products_by_Department['Percentage'] = np.round(100* Products_by_Department['Total Products']\
                                                / Products_by_Department['Total Products'].sum(),2)
Products_by_Department.sort_values(by='Total Products', ascending=False, inplace=True)

Colors = ['LightGreen']
LC = 'DarkGreen'
fig = px.bar(Products_by_Department, x = 'Department', y='Total Products', text = 'Percentage',
                   color_discrete_sequence= Colors,
                   hover_data= Products_by_Department.columns)
fig.update_layout(plot_bgcolor= 'white', barmode='stack')
fig.update_traces(marker_line_color= LC, marker_line_width=1, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray', range=[0, 7e3])
fig.update_layout(title={'text': 'Total Products in Each Departments', 'x':0.5, 'y':0.94,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

It can be seen that Personal Care and Snacks are the best seller departments.

Products Distributions in Each Aisle

def Search_List(Key, List): return [s for s in List if Key in s]
myColors = list(mcolors.CSS4_COLORS.keys())
Temp = []
Temp.extend(Search_List('gray', myColors))
Temp.extend(Search_List('grey', myColors))
Temp.extend(Search_List('black', myColors))
myColors = list(set(myColors)-set(Temp))
#
Products_by_Aisle = Products_Detailed.groupby(["Department", "Aisle"])["Product ID"].count().\
                    to_frame('Total Products').reset_index(drop = False)
Products_by_Aisle['Percentage'] = np.round(100* Products_by_Aisle['Total Products']\
                                                / Products_by_Aisle['Total Products'].sum(),2)
Products_by_Aisle = Products_by_Aisle.sort_values(by='Total Products', ascending=False)

Products_by_Aisle = Products_by_Aisle.loc[(Products_by_Aisle['Department'] != 'Missing') &
                                          (Products_by_Aisle['Aisle'] != 'Missing') &
                                          (Products_by_Aisle['Aisle'] != 'Other')]
for d in Products_by_Aisle['Department'].unique().tolist():
    Products_by_Aisle.loc[Products_by_Aisle['Department'] == d , 'Percentage'] = \
    np.round(100* Products_by_Aisle.loc[Products_by_Aisle['Department'] == d , 'Total Products']\
    /Products_by_Aisle.loc[Products_by_Aisle['Department'] == d , 'Total Products'].sum(),2)

# Plot
font = FontProperties()
font.set_weight('bold')
    
Temp = list(np.linspace(0, 100, 6))
m = Products_by_Aisle['Percentage'].max()
for j in range(len(Temp)-1):
    if Temp[j] <= m < Temp[j+1]:
        m = Temp[j+1]
        break
        
N = len(Products_by_Aisle.Department.unique())
fpr = 4; C = 0; Ind = list(np.arange(0, N, fpr))
fig, ax = plt.subplots(nrows=int(np.ceil(N/fpr)), ncols=fpr, figsize=(16,30), sharey = True,
                       gridspec_kw =  dict(hspace=1.4, wspace=.0))
ax = ax.ravel()
_ = fig.delaxes(ax[-1])
Palette = ['Purples', 'Blues', 'Greens', 'Oranges']*5
EdgeColor = ['Indigo', 'Navy', 'ForestGreen','Salmon']*5
Hatch = ['//', 'O', '--', '\\']*5
for (aisle, PBA), ax in zip(Products_by_Aisle.groupby(['Department']), ax.flatten()):
    _ = sns.barplot(PBA['Aisle'], PBA['Percentage'] , ax=ax, palette= Palette[C], edgecolor=EdgeColor[C], hatch= Hatch[C])
    _ = sns.barplot(PBA['Aisle'], PBA['Percentage'] , ax=ax, facecolor = 'None', edgecolor='k')
    if C in Ind:
        _ = ax.set(xlabel = 'Aisles', ylabel='Percentage', ylim = [0, 100])
    else:
        _ = ax.set(xlabel = 'Aisles', ylabel=None, ylim = [0, m])
    _ = ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
    _ = ax.set_title(aisle)
    C +=1
    
_ = fig.suptitle(t = 'Product Distributions', y = .91,
                 fontproperties=font, fontsize = 16)

Colors = myColors.copy()
LC = 'Black'
fig = px.bar(Products_by_Aisle, x = 'Aisle', y='Total Products', color = 'Department',
             text = 'Total Products', color_discrete_sequence= Colors, hover_data= ['Department', 'Total Products'])
fig.update_layout(plot_bgcolor= 'white', barmode='stack')
fig.update_traces(marker_line_color= LC, marker_line_width=1, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray', range=[0, 1.4e3])
fig.update_layout(title={'text': 'Total Products in Each Aisles', 'x':0.5, 'y':0.94,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Candy chocolate, chips pretzels, and cookies cakes are the best selling products.

Sale Distributions

Best Selling Departments

Orders_Detailed = orders[['user_id', 'order_id']].merge(order_products_train[['order_id', 'product_id']],
                                                        how='inner', left_on='order_id', right_on='order_id')
Orders_Detailed.columns = [x.replace('_',' ').title().replace('Id','ID') for x in Orders_Detailed.columns.tolist()]
Orders_Detailed = Orders_Detailed.merge(Products_Detailed, how='inner', left_on='Product ID', right_on='Product ID')
#
Orders_by_Department = Orders_Detailed.groupby('Department')['Order ID'].count().\
                       to_frame('Total Orders').reset_index(drop = False)
Orders_by_Department['Percentage'] = np.round(100* Orders_by_Department['Total Orders']\
                                              / Orders_by_Department['Total Orders'].sum(),2)

Colors = ['MediumVioletRed']
LC = 'Indigo'
fig = px.bar(Orders_by_Department, x = 'Department', y= 'Percentage', text = 'Total Orders', color_discrete_sequence= Colors,
             hover_data = Orders_by_Department.columns)
fig.update_layout(plot_bgcolor= 'white', barmode='stack')
fig.update_traces(marker_line_color= LC, marker_line_width=1, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray', range=[0, 30])
fig.update_layout(title={'text': 'Best Selling Departments', 'x':0.5, 'y':0.94,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

Best Selling Aisles

Products_by_Aisle = Orders_Detailed.groupby(["Department", "Aisle"])["Product ID"].count().\
                    to_frame('Total Products').reset_index(drop = False)
Products_by_Aisle['Percentage'] = np.round(100* Products_by_Aisle['Total Products']\
                                                / Products_by_Aisle['Total Products'].sum(),2)
Products_by_Aisle = Products_by_Aisle.sort_values(by='Total Products', ascending=False)
Products_by_Aisle.head().style.hide_index()

Products_by_Aisle = Products_by_Aisle.loc[(Products_by_Aisle['Department'] != 'Missing') &
                                          (Products_by_Aisle['Aisle'] != 'Missing') &
                                          (Products_by_Aisle['Aisle'] != 'Other')]
for d in Products_by_Aisle['Department'].unique().tolist():
    Products_by_Aisle.loc[Products_by_Aisle['Department'] == d , 'Percentage'] = \
    np.round(100* Products_by_Aisle.loc[Products_by_Aisle['Department'] == d , 'Total Products']\
    /Products_by_Aisle.loc[Products_by_Aisle['Department'] == d , 'Total Products'].sum(),2)

# Plot
font = FontProperties()
font.set_weight('bold')
    
Temp = list(np.linspace(0, 100, 6))
m = Products_by_Aisle['Percentage'].max()
for j in range(len(Temp)-1):
    if Temp[j] <= m < Temp[j+1]:
        m = Temp[j+1]
        break
        
N = len(Products_by_Aisle.Department.unique())
fpr = 4; C = 0;
Ind = list(np.arange(0, N, fpr))
fig, ax = plt.subplots(nrows=int(np.ceil(N/fpr)), ncols=fpr, figsize=(16,30), sharey = True,
                       gridspec_kw =  dict(hspace=1.4, wspace=.0))
ax = ax.ravel()
_ = fig.delaxes(ax[-1])
Palette = ['Purples', 'Blues', 'Greens', 'Oranges']*5
EdgeColor = ['Indigo', 'Navy', 'ForestGreen','Salmon']*5
Hatch = ['//', 'O', '--', '\\']*5
for (aisle, PBA), ax in zip(Products_by_Aisle.groupby(['Department']), ax.flatten()):
    _ = sns.barplot(PBA['Aisle'], PBA['Percentage'] , ax=ax, palette= Palette[C], edgecolor=EdgeColor[C], hatch= Hatch[C])
    _ = sns.barplot(PBA['Aisle'], PBA['Percentage'] , ax=ax, facecolor = 'None', edgecolor='k')
    if C in Ind:
        _ = ax.set(xlabel = 'Aisles', ylabel='Percentage', ylim = [0, 100])
    else:
        _ = ax.set(xlabel = 'Aisles', ylabel=None, ylim = [0, m])
    _ = ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
    _ = ax.set_title(aisle)
    C +=1
    
_ = fig.suptitle(t = 'Product Sale Distributions', y = .91,
                 fontproperties=font, fontsize = 16)


##
Colors = myColors.copy()
LC = 'Black'
fig = px.bar(Products_by_Aisle, x = 'Aisle', y='Total Products', color = 'Department',
             text = 'Total Products', color_discrete_sequence= Colors,
                   hover_data= ['Department', 'Total Products'])
fig.update_layout(plot_bgcolor= 'white', barmode='stack')
fig.update_traces(marker_line_color= LC, marker_line_width=1, opacity=1)
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray', range=[0, 2e5])
fig.update_layout(title={'text': 'Best Selling Aisles', 'x':0.5, 'y':0.94,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

---

References

1. Kaggle Dataset: Instacart Market Basket Analysis

---